EX-08. Email Marketing Platforms. AI visibility audit.

Mailchimp

Mailchimp leads Email Marketing Platforms in both category-named and problem-first responses.

Across 270 responses, Mailchimp appears in 44.4% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

16/63

Mailchimp in problem-first

Mailchimp's presence in symptom-shaped queries.

Overall category presence: 120 of 270 (44.4%). In problem-first specifically, 40 of 63 responses in this bucket name no vendor at all (63.5%). That's empty inventory the leader could plausibly claim.

How AI answered a real buyer question in this category.

Query. Bucket 8. Problem-first. Excerpted from published audit.

my open rates are dropping and I don't know why

Declining open rates are frustrating, but they're usually fixable once you identify the cause. Here are the most common culprits, roughly in order of likelihood:

## 1. Deliverability issues (inbox vs. spam)

This is the #1 hidden killer. Your emails might be landing in spam/promotions without you knowing.

- **Check your sender reputation** at tools like Google Postmaster Tools, Sender Score, or Mail-Tester

- **Verify SPF, DKIM, and DMARC** are properly configured

- **Send a test** to seed accounts across Gmail, Outlook, Yahoo, etc.

## 2. List decay

Email lists naturally degrade ~20-30% per year.

- Old subscribers lose interest

- People change jobs (B2B lists decay faster)

- Inactive addresses hurt your sender reputation, which then hurts everyone else's opens

**Fix:** Run a re-engagement campaign, then sunset chronically inactive subscribers.

## 3. Subject lines & sender name

- Are you A/B testing? If not, start

- Is your "From" name consistent and recognizable?

- Are you triggering spam filters with words like "FREE," excessive punctuation, or ALL CAPS?

## 4. Apple Mail Privacy Protection (MPP) distortion

Since 2021, Apple pre-fetches images, which *inflates* open rates. If Apple recently changed something or your audience shifted away from Apple Mail, your "real" open rate is being revealed. Compare click rates—if those are stable, opens may just be a measurement...

Vendors named in this response NONE

Appearance rank and pick-share rank tell different stories.

Every brand ranked two ways. Appearance = how often the brand is named at all. Pick share = how often the brand is the SOLE or PRIMARY pick, as a percentage of all 270 responses. The gap between the two is the recommendation gap.

#	Brand	Appear	App %	Pick %
01	Mailchimp	120	44.4%	1.1%
02	Klaviyo	97	35.9%	16.7%
03	MailerLite	88	32.6%	5.6%
04	Mailerlite	88	32.6%	5.9%
05	Kit	82	30.4%	3.3%
06	ConvertKit	77	28.5%	4.1%
07	Brevo	74	27.4%	4.4%
08	Beehiiv	74	27.4%	15.6%
09	ActiveCampaign	52	19.3%	1.5%
10	Sendinblue	52	19.3%	1.9%
11	Substack	49	18.1%	0.4%
12	Omnisend	45	16.7%	1.1%

Mailchimp appears in 44.4% of responses and is the sole or primary pick in 1.1%. That is a 43.3-point gap. 40 of 120 appearances land as NEUTRAL (cited but not recommended). 25 appearances are UNFAVORABLE.

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	ActiveCampaign	25	Mailchimp 24
Comparison	Klaviyo	26	Mailchimp 18
Segment fit	MailerLite	25	Mailerlite 25
Evaluation	Mailchimp	17	Klaviyo 11
Pricing	Brevo	18	MailerLite 17
Trust	Mailchimp	12	Beehiiv 9
Switching	Klaviyo	9	Mailchimp 9
Problem-first	Mailchimp	16	Klaviyo 10

Problem-first zoom.

The highest-leverage queries: the buyer describes a symptom rather than naming the category.

#	Brand	Count	%
01	Mailchimp	16	25.4%
02	Klaviyo	10	15.9%
03	HubSpot	7	11.1%
04	SendGrid	7	11.1%
05	Brevo	4	6.3%
n/a	Zero-vendor responses	40	63.5%

The domains the assistants pulled from.

Two lists. Vendor-owned and editorial sources on the left. SEO listing sites on the right. The split reveals which channel is doing the citation work.

Established, own content

beehiiv.com Beehiiv71
mailchimp.com Mailchimp64
brevo.com Brevo63
moosend.com Moosend52
omnisend.com Omnisend39
help.klaviyo.com Klaviyo39
klaviyo.com Klaviyo33
drip.com Drip24
blog.hubspot.com HubSpot15
activecampaign.com ActiveCampaign13

SEO listing sites

sequenzy.com109

Methodology and caveats.

Run date. 2026-07-24 UTC.
Sample. 30 buyer questions across 8 buckets, 3 samples per engine. Target 270 responses.
Engines. anthropic, openai, perplexity.
API is not the consumer app. Directionally representative, not literal.
Answers vary. Every prompt sampled 3 times per engine. Frequencies, not binaries.
Two-stage matcher (v0.4). Brand extraction is case-insensitive, word-boundary-anchored. Vendors whose canonical name overlaps with a common English word are flagged ambiguous in the config, and every candidate hit goes to a second-stage LLM adjudication (Claude Haiku 4.5) that reads the surrounding context and answers whether the token is the company or generic usage.
Recommendation classification (v0.5). Every confirmed brand mention runs through a second LLM pass (Claude Haiku 4.5) that classifies the treatment of that brand in that response as one of six codes: SOLE (only vendor named as the answer), PRIMARY (named as the top pick), ALTERNATIVE (named in a short recommended set), CONDITIONAL (recommended only for a stated narrow case), NEUTRAL (mentioned without endorsement, or cited as a source only), or UNFAVORABLE (named with a negative or dismissive frame). Pick share of total = (SOLE + PRIMARY) ÷ total responses.
No editorializing. This exhibit reads the data. It does not claim causation.
Right of reply. Any named brand can request their own free audit. Findings publish alongside.

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